A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Novel method prices call options using Pearson diffusion processes.
Study benchmarks TSC algorithms in distinguishing diffusions using the likelihood ratio test.
We compute exact values respectively bounds of "distances" - in the sense of (transforms of) power divergences and relative entropy - between two discrete-time Galton-Watson branching processes with immigration GWI for which the offspring as well as the immigration is arbitrarily Poisson-distributed (leading to arbitra…
New bounds for Neyman-Pearson region using -divergences.
The article generalizes Pearson correlation to Riemannian manifolds.
A novel unified Bayesian framework for network detection is developed, under which a detection algorithm is derived based on random walks on graphs. The algorithm detects threat networks using partial observations of their activity, and is proved to be optimum in the Neyman-Pearson sense. The algorithm is defined by a …
Financial markets analyzed by reducing correlation matrix complexity.
The paper extends Pearson correlation to multi-variables, useful for noise measurement and feature selection.
Model predicts epileptic seizures with high accuracy using EEG signals.
Adapts Neyman-Pearson classification for both source and target distribution shifts.
We examine the efficiency of the Asymmetric Power ARCH (APARCH) model in the case where the residuals follow the standardized Pearson type IV distribution. The model is tested with a variety of loss functions and the efficiency is examined via application of several statistical tests and risk measures. The results indi…
For time series comparisons, it has often been observed that z-score normalized Euclidean distances far outperform the unnormalized variant. In this paper we show that a z-score normalized, squared Euclidean Distance is, in fact, equal to a distance based on Pearson Correlation. This has profound impact on many distanc…
Combines cost-sensitive and Neyman-Pearson paradigms for better binary classification.
Neyman-Pearson testing improves goodness of fit in detecting new physics.
New RDPC dissimilarity measure improves time series clustering.
Entropy measures in their various incarnations play an important role in the study of stochastic time series providing important insights into both the correlative and the causative structure of the stochastic relationships between the individual components of a system. Recent applications of entropic techniques and th…
Characterizes distribution-free rates in unbalanced classification problems.
This study uses local Gaussian correlation to analyze stock return tails, revealing more sensitive network properties.
The Pearson distance between a pair of random variables with correlation , namely, 1-, has gained widespread use, particularly for clustering, in areas such as gene expression analysis, brain imaging and cyber security. In all these applications it is implicitly assumed/required that the distance …
The paper tackles Neyman-Pearson classification control issues.
USP test improves on Pearson's chi-squared and -test for independence.
In this short report, we investigate the ability of the DCCA coefficient to measure correlation level between non-stationary series. Based on a wide Monte Carlo simulation study, we show that the DCCA coefficient can estimate the correlation coefficient accurately regardless the strength of non-stationarity (measured b…
Unified framework for Bayes-optimal classifiers under group fairness.
New method corrects bias in density ratio estimation for missing data.
Develops NPMC method for noisy labels, improving multiclass classification accuracy.
Develops algorithms for multi-class Neyman-Pearson classification with cost sensitivity.
The paper develops approximations for Pearson's chi-square statistic and applies them to confidence intervals.
The market events of 2007-2009 have reinvigorated the search for realistic return models that capture greater likelihoods of extreme movements. In this paper we model the medium-term log-return dynamics in a market with both fundamental and technical traders. This is based on a Poisson trade arrival model with variable…
Most existing binary classification methods target on the optimization of the overall classification risk and may fail to serve some real-world applications such as cancer diagnosis, where users are more concerned with the risk of misclassifying one specific class than the other. Neyman-Pearson (NP) paradigm was introd…
Enhanced metrics for multiclass classification improve on existing methods.
A new method detects and displays pairwise dependence between variates.
A large body of research into semantic textual similarity has focused on constructing state-of-the-art embeddings using sophisticated modelling, careful choice of learning signals and many clever tricks. By contrast, little attention has been devoted to similarity measures between these embeddings, with cosine similari…
High-dimensional, large-sample astrophysical databases of galaxy clusters, such as the Chandra Deep Field South COMBO-17 database, provide measurements on many variables for thousands of galaxies and a range of redshifts. Current understanding of galaxy formation and evolution rests sensitively on relationships between…
Stock price movement reveals complex interdependencies that are simplified through linear correlation.
A neural network for online NP classification with reduced complexity.
This paper uses rank correlation methods to construct MSTs from financial returns, finding them more stable and robust.
The paper studies statistical properties of CART regression trees.
Motivated by problems of anomaly detection, this paper implements the Neyman-Pearson paradigm to deal with asymmetric errors in binary classification with a convex loss. Given a finite collection of classifiers, we combine them and obtain a new classifier that satisfies simultaneously the two following properties with …
This paper presents two approaches for filter design based on stochastic distances for intensity speckle reduction. A window is defined around each pixel, overlapping samples are compared and only those which pass a goodness-of-fit test are used to compute the filtered value. The tests stem from stochastic divergences …
Optimal selective classification using likelihood ratios improves model reliability.
Investigation of the market graph attracts a growing attention in market network analysis. One of the important problem connected with market graph is to identify it from observations. Traditional way for the market graph identification is to use a simple procedure based on statistical estimations of Pearson correlatio…
Motivated by optimal investment problems in mathematical finance, we consider a variational problem of Neyman-Pearson type for law-invariant robust utility functionals and convex risk measures. Explicit solutions are found for quantile-based coherent risk measures and related utility functionals. Typically, these solut…
Paper establishes a formula linking model performance to insurance loss ratio.
The gain-loss asymmetry, observed in the inverse statistics of stock indices is present for logarithmic return levels that are over , and it is the result of the non-Pearson type auto-correlations in the index. These non-Pearson type correlations can be viewed also as functionally dependent daily volatilities, ext…
Network analysis reveals changing cryptocurrency market leaders.
In the problem of domain adaptation for binary classification, the learner is presented with labeled examples from a source domain, and must correctly classify unlabeled examples from a target domain, which may differ from the source. Previous work on this problem has assumed that the performance measure of interest is…
New algorithm controls type I error in NP classification under label noise.
Recently the interest of researchers has shifted from the analysis of synchronous relationships of financial instruments to the analysis of more meaningful asynchronous relationships. Both of those analyses are concentrated only on Pearson's correlation coefficient and thus intraday lead-lag relationships associated wi…